Precision Medicine and the Reclassification of Cancer Divide and

Transcription

Precision Medicine and the Reclassification of Cancer
Divide and Conquer
Razelle Kurzrock, MD
Senior Deputy Director , Clinical Science
Director, Center for Personalized Cancer Therapy
Director, Clinical Trials Office
Chief, Division of Hematology/Oncology
UCSD Moores Cancer Center
Center for Personalized Cancer Therapy
at Moores Cancer Center
Developmental Therapeutics
Phase I Trials/
Genomics/Immunotherapy
San Diego
Biotech,
Pharma,
Genomics
Discovery
to
Bedside
Enabling
Program
Rare
Tumor
Clinic
Financial Aid
Adolescent
and Young
Adult
Clinic
Molecular
Tumor
Board
Hereditory
Cancer
Predispositon
Genetic
Counselling
Molecular
Pathway
Clinic
Laboratory Processing
UCSD, Salk, Scripps, Sanford-Burnham
UCSD
Super
Computer
Center
Kurzrock Experience
MD Anderson Phase I Trials / Largest Department World Wide
1400
140
127
118
1200
120
1175
102
1175
1100
1132
100
80
800
790
80
821
65
600
60
565
583 605
490
38
400
40
24
445
375
200
20
153
191
0
0
FY2005
FY2006
FY2007
# Enrolled
FY2008
FY2009
# Enrolled, including all U01
FY2010
# Trials
FY2011
# of Trials
# of Patients
1000
Question: Is it precision medicine or
personalized medicine?
Answer: Both
“Precisionalized Medicine”
The Pillars of Precision Cancer Medicine
Genomics Immunotherapy
Precision Medicine
Lessons Learned
 Use combinations of matched drugs for metastatic or
complex tumors
 Treat newly-diagnosed patients
 Omics is a disruptive technology; retrofitting the reality
unveiled into traditional paradigms is suboptimal
 Harness the immune system
 Transformative changes will require new models for
clinical research and practice
Why are cancers difficult to treat?
Divide and Conquer
Agents work only
in those with
a sensitizing
aberration
Braiteh….Kurzrock, MCT 2007
Munoz J, Swanton C, Kurzrock R, Molecular Profiling and the Reclassification of Cancer;
Am Soc Clin Oncol Educ Book. 2013:
Sharma, Nat Rev Cancer 2010
What can patients expect from traditionally
approved drugs ?
Drug
Tumor
Survival Gain
Complete
remission
gemcitabine
pancreas
1.5 months
≈ 0%
bevacizumab
colon
2.2 months
≈ 0%
erlotinib
pancreas
11 days
≈ 0%
bevacizumab
NSCLC
2 months
≈ 0%
sorafenib
renal
2 months
≈ 0%
temozolamide
glioblastoma
2.5 months
≈ 0%
docetaxel
prostate
2.4 months
≈ 0%
cetuximab
colon
1.5 months
≈ 1-2 %
Master Protocol
Profile-Related Evidence Determining
Individualized Cancer Therapy
PREDICT
• Histology-Independent targeted approach
• Multiple molecular aberrations assessed
• Patients matched with targeted agents
The Reclassification of Cancer
PIK3CA mutations were found in 10% of
1,000 patients with advanced cancers
• Endometrial cancers (29%)
• Breast cancers (24%)
• Colon cancers (17%)
• Ovarian cancers (14%)
• Lung cancer (13%)
• Head and neck squamous cell cancers (13%)
• Pancreatic cancers (13%)
Molecular aberrations do not
segregate well by organ of origin
Matching patients with targeted drugs increases
response rates
Matched therapy
N=175
Complete/Partial Response = 27%
Therapy without matching
N=116
Complete/Partial Response = 5%
100
90
80
70
60
50
40
30
20
10
CR: 4 (2%)
PR: 43 (25%)
SD>6m: 40 (23%)
Change in tumor size, %
Change in tumor size, %
p<.0001
-10
-20
-30
-40
-50
-60
-70
-80
-90
-100
Patients
100
90
80
70
60
50
40
30
20
10
CR: 0 (0%)
PR: 6 (5%)
SD>6m: 12 (10%)
-10
-20
-30
-40
-50
-60
-70
-80
-90
-100
Patients
Janku….Kurzrock, MCT, 2011; Tsimberidou….. Kurzrock, CCR, 2012;
Janku…..Kurzrock, JCO, 2012; Janku…..Kurzrock, Cell Reports, 2014
Partnering with the
UCSD SuperComputer Center
What if every patient with
metastatic disease is different?
Malignant Snowflakes
Tip of the Iceberg in Cancer
Genomics
Transcriptome
Proteomics
Epigenetic changes
GENOMICS / PRECISION THERAPY
Epidermal Growth Factor Receptor (EGFR)
In Silico Modelling in Lung Cancer
3D In Silico Modeling
Sensitizing Mutations
X
Cetuximab
Exon 20
Resistance Mutations
Zhou et al., Lancet Oncol 2011
From Herbst, Heymach and Lippman, N Engl J Med 2008
Tsigelny….Kurzrock, Oncotarget
Kurzrock et al., Oncotarget Feb 28, 2015
Strategies
Customized
Combinations and
Immunotherapy
for Advanced
Disease
Treat
Newly-Diagnosed
Disease
WIN confidential & proprietary
17
THANK YOU
Transforming Outcomes
in Solid Tumors?
Is It About Time?
Lessons from the Chronic Myelogenous Leukemia
(CML) Story
A Fatal Disease Transformed
•
•
Median survival in 1980s was about 4 years
Median survival in 2012 is 20+ years
2012
1981
Stage
Response Rate ( %)
Key factors leading to the revolution in
outcome of chronic myelogenous disease
• Key factors:
– Known driver target (Bcr-Abl)
– Targeted agent (imatinib)
– Treat newly-diagnosed patients
2012
1981
Metastases = Blast Crisis in Leukemia
Tumor Microhetergoeneity
• Molecular profile
can differ even
within the single
lesion
• Discrepancy
between molecular
profile of primary
and metastic lesion
(~20%).
PD-L1 and PD-L2 are ligands for the PD-1 receptor present on T
Harnessing the immune system
Immunotherapy is revolutionizing cancer care
Metastatic Melanoma: Long-term remissions
Robert et al. ….. Melanoma…..N Engl J Med 2015; 372:320-330
Combinatorial immune blockade is likely the rule,
not the exception
• ASCO 2014 update
• 2 year survival rate- 79%
• Comparison: dacarbazine monotherapy 2 year survival rate- 18%
• Prior therapies (1-3+) in 38%
Wolchok JD et al. N Engl J Med 2013;369:122-133
Predicting super-responders to immunotherapy
Biomarker
• PDL-1 negative: 0-17%
• PDL-1 positive: 36-100%
Patel and Kurzrock, MCT 2015
Unique characteristics
• Delayed responses with initial progression
• Subset of patients with advanced disease that have
long-term complete remission (?cure)
Liquid Biopsy Program
Doing genomics on DNA from a small tube of blood sample
No tissue biopsy
~700 patients
Liquid Biopsy Program
 Blood
 Urine
 Ascites
Theoretically samples shed
DNA from multiple metastatic sites.
Tracking EGFR T790M Mutations
in ULung Cancer
Early Detection of Progression
scites in Lung Cancer
GENOMICS / LIQUID BIOPSIES
Urine
Urine T790M Copies/100K GE
100
Progresssion on
CAT scan
T790M
detected
80
60
40
20
IRB-approved HRPP 130794; H. Husain and R. Kurzrock (manuscript in prep)
9/18/2014
8/9/2014
6/30/2014
5/21/2014
4/11/2014
0
Liquid Biopsy
(N = 171 Patients)
Cancer
Circulatory Tumor DNA
N = 54 genes
No. of Patients/Total (%)
All
99/171 (58%)
Glioblastoma
9/33
Actionable
67/171 (39%)
Healthy Volunteer
1/222 (0.45%) [p53]
(37%)
Liquid Biopsy
(N = 171 Patients)
Cancer
N = 54 genes
All
99/171 (58%)
Glioblastoma
9/33
Actionable
67/171 (39%)
Healthy Volunteer
1/222 (0.45%) [p53]
(37%)
Liquid Biopsy
(N = 171 Patients)
Cancer
N = 54 genes
All
99/171 (58%)
Glioblastoma
9/33
Actionable
67/171 (39%)
Healthy Volunteer
1/222 (0.45%) [p53]
(37%)
Liquid Biopsy
(N = 171 Patients)
Cancer
N = 54 genes
All
99/171 (58%)
Glioblastoma
9/33
Actionable
67/171 (39%)
Healthy Volunteer
1/222 (0.45%) [p53]
(37%)
Liquid Biopsy
(N = 171 Patients)
Cancer
Circulating Tumor DNA
N = 54 genes
All
99/171 (58%)
Glioblastoma
9/33
Actionable
67/171 (39%)
Healthy Volunteer
1/222 (0.45%) [p53]
(37%)
Detecting EGFR Amplifications
in Ascites in Lung CancerCancer
Case
Age/Sex
No.
1
Diagnosis
64/man Adenocarcinoma
of the lung,
metastasis to
pleura and
peritoneum
a
Tissue NGS
c
CDK4 amplification
MDM2 amplification
[Also, PCR-based assay (Response Genetics) of
primary lung tumor in December 2010 was negative
for EGFR, ROS1 and ALK aberrations]
(March 2013, Pleural mass)
b
Ascites ctDNA
Somatic Mutations:
EGFR amplification
Total CNVs detected: 23
(May 2014)
Liquid biopsy applications
Customized
combinations for
advanced disease.
Need to know all
genomic
aberrations from
multiple metastases
Follow newlydiagnosed disease
Monitor resistance
WIN confidential & proprietary
39
Precision Medicine
Lessons from meta-analyses
of 70,253 patients
Meta-Analyses Conducted
1) Trials leading to FDA approval from
trastuzumab (1998) until June 2013
 38,104 patients; 112 trials
2) Phase II studies published between
2 32,149 patients; 570 trials
Maria Schwaederle, PharmD
Summary of results
Multivariable analysis: N = 38,104
Randomized trials meta regression
Characteristic
All trials meta-regression (RR) and
weighted pooled multilinear
regression (PFS/OS)
P-value
RRR meta-regression
Characteristic
Personalized therapy strategy
0.03
P-value
Response rate
Ctrl arm placebo vs. active drug
<0.001
<0.001
Cross-over allowed
<0.001
Chemotherapy-naïve patients
<0.001
Hematologic tumor vs solid
<0.001
Progression Free Survival
<0.001
Ctrl arm placebo vs. active drug
<0.001
0.004
Cross-over allowed
<0.001
Overall Survival
0.07
0.006
Progression Free Survival
0.002
Overall Survival
0.041
8
35
30
25
Months
Response rate (%)
20
20
7
16
6
14
5
4
15
3
10
2
5
1
0
0
Personalized
Not
personalized
Pooled analysis
Meta-analysis
Median OS
(Months, CI 95%)
18
Months
40
Median PFS
(Months, CI 95%)
Response Rate
(%, CI 95%)
12
10
8
6
4
2
Personalized
Not
personalized
0
Personalized
Not
personalized
CONCLUSIONS
• Non-personalized targeted arms led to poorer
outcomes than cytotoxics arms
(All P<0.0001, except P=0.048 for OS meta-analysis).
POOLED
Analysis
Worst outcome
Best outcome
Meta-analysis
ARMS type
RR PFS
OS
RR
(%) (Mos) (Mos) (%)
PFS
OS
(Mos) (Mos)
Non-personalized
targeted
4
2.6
8.7
7.5
2.5
8.3
Cytotoxic
12
3.3
9.4
16.1
3.3
9.3
Personalized
targeted
30
6.9
15.9
31.3
6.1
13.7
THANK YOU
for your time and interest
Questions??
[email protected]
[email protected]

Precision Medicine and the Reclassification of Cancer Divide and

Transcription

Similar documents